Chapter 6 Community composition

load("data/data.Rdata")

6.1 Taxonomy overview

6.1.1 Stacked barplot

genome_counts_filt_met<-genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  left_join(., sample_metadata, by = join_by(sample == EHI_number)) %>% #append sample metadata
  filter(count > 0) #filter 0 counts

genome_counts_filt_met$Elevation<-as.factor(genome_counts_filt_met$Elevation)
# Create an interaction variable for elevation and sample
genome_counts_filt_met$interaction_var <- interaction(genome_counts_filt_met$sample, genome_counts_filt_met$Elevation)

# Plot stacked barplot
ggplot(genome_counts_filt_met, aes(x=interaction_var,y=count,fill=phylum, group=phylum))+ #grouping enables keeping the same sorting of taxonomic units
    geom_bar(stat="identity", colour="white", linewidth=0.1)+ #plot stacked bars with white borders
    scale_fill_manual(values=phylum_colors) +
    labs(y = "Relative abundance", x="Elevation (m)") +
    guides(fill = guide_legend(ncol = 3)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
          panel.background = element_blank(),
          panel.border = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black"),
          legend.position = "top",
    legend.title = element_blank(),
    legend.text = element_text(size=7))+
    scale_x_discrete(labels = function(x) gsub(".*\\.", "", x)) +
    facet_wrap(~Transect, scales = "free", labeller = as_labeller(function(label) gsub(".*\\.", "", label))) #only show elevation label

6.1.1.1 Number of bacteria phyla

[1] 16

6.1.1.2 Bacteria phyla in Aisa transect

phylum_summary_aisa <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
  left_join(genome_metadata, by = join_by(genome == genome)) %>%
  group_by(sample,phylum) %>%
  summarise(relabun=sum(count)) %>%
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
  filter(Transect=="Aisa")%>%
  filter(relabun > 0)

# Number of bacterial phyla Aisa
length(unique(phylum_summary_aisa$phylum))

[1] 13

# Bacteria phyla
phylum_summary_aisa %>%
    group_by(phylum) %>%
    summarise(total_mean=mean(relabun*100, na.rm=TRUE),
              total_sd=sd(relabun*100, na.rm=TRUE))  %>%
    mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(phylum,total) %>% 
    tt()

phylum	total
p__Bacillota_A	42.18±15.99
p__Bacteroidota	39.09±17.57
p__Pseudomonadota	5.27±6.75
p__Bacillota	3.99±3.67
p__Verrucomicrobiota	3.53±3.26
p__Campylobacterota	3.29±3.44
p__Desulfobacterota	2.48±1.93
p__Fusobacteriota	1.35±1.33
p__Bacillota_C	1.29±1.53
p__Cyanobacteriota	0.82±0.49
p__Chlamydiota	0.7±0.63
p__Bacillota_B	0.53±0.41
p__Actinomycetota	0.18±0.09

6.1.1.3 Bacteria phyla in Aran transect

phylum_summary_aran <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
  left_join(genome_metadata, by = join_by(genome == genome)) %>%
  group_by(sample,phylum) %>%
  summarise(relabun=sum(count)) %>%
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
  filter(Transect=="Aran")%>%
  filter(relabun > 0)

# Number of bacterial phyla Aran
length(unique(phylum_summary_aran$phylum))

[1] 14

# Bacteria phyla
phylum_summary_aran %>%
    group_by(phylum) %>%
    summarise(total_mean=mean(relabun*100, na.rm=TRUE),
              total_sd=sd(relabun*100, na.rm=TRUE))  %>%
    mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(phylum,total) %>% 
    tt()

phylum	total
p__Bacillota_A	41.7±16.74
p__Bacteroidota	36.85±14.71
p__Pseudomonadota	7.27±11.02
p__Campylobacterota	5.5±8.27
p__Bacillota	4.64±5.26
p__Fusobacteriota	4.01±8.68
p__Desulfobacterota	2.75±2.22
p__Verrucomicrobiota	2.38±3.15
p__Spirochaetota	1.22±1.19
p__Bacillota_C	1.14±1.18
p__Cyanobacteriota	0.72±0.54
p__Deferribacterota	NA
p__Bacillota_B	0.44±0.45
p__Actinomycetota	0.3±0.26

6.1.1.4 Bacteria phyla in Sentein transect

phylum_summary_sentein <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
  left_join(genome_metadata, by = join_by(genome == genome)) %>%
  group_by(sample,phylum) %>%
  summarise(relabun=sum(count)) %>%
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
  filter(Transect=="Sentein")%>%
  filter(relabun > 0.)

# Number of bacterial phyla Sentein
length(unique(phylum_summary_sentein$phylum))

[1] 15

# Bacteria phyla
phylum_summary_sentein %>%
    group_by(phylum) %>%
    summarise(total_mean=mean(relabun*100, na.rm=TRUE),
              total_sd=sd(relabun*100, na.rm=TRUE))  %>%
    mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(phylum,total) %>% 
    tt()

phylum	total
p__Bacteroidota	40.72±14.71
p__Bacillota_A	34.82±14.88
p__Bacillota	6.17±10.94
p__Pseudomonadota	5.19±5.3
p__Campylobacterota	4.8±5.42
p__Desulfobacterota	4.77±8.22
p__Spirochaetota	4.76±5.8
p__Chlamydiota	3.19±3.26
p__Fusobacteriota	2.17±5.62
p__Verrucomicrobiota	1.34±1.49
p__Cyanobacteriota	0.78±0.37
p__Actinomycetota	0.77±1.29
p__Bacillota_C	0.61±0.51
p__Bacillota_B	0.16±0.07
p__Deferribacterota	NA

6.1.1.5 Bacteria phyla in Tourmalet transect

phylum_summary_tourmalet <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
  left_join(genome_metadata, by = join_by(genome == genome)) %>%
  group_by(sample,phylum) %>%
  summarise(relabun=sum(count)) %>%
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
  filter(Transect=="Tourmalet")

# Number of bacterial phyla Tourmalet
length(unique(phylum_summary_tourmalet$phylum))

[1] 16

# Bacteria phyla
phylum_summary_tourmalet %>%
    group_by(phylum) %>%
    summarise(total_mean=mean(relabun*100, na.rm=TRUE),
              total_sd=sd(relabun*100, na.rm=TRUE))  %>%
    mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(phylum,total) %>% 
    tt()

phylum	total
p__Bacillota_A	41.87±15.42
p__Bacteroidota	39.29±15.9
p__Bacillota	5.87±5.94
p__Pseudomonadota	5.3±5.15
p__Campylobacterota	2.32±2.71
p__Desulfobacterota	1.58±2.02
p__Verrucomicrobiota	0.94±1.13
p__Bacillota_C	0.83±0.53
p__Cyanobacteriota	0.53±0.82
p__Fusobacteriota	0.4±0.81
p__Actinomycetota	0.39±0.73
p__Spirochaetota	0.23±0.51
p__Deferribacterota	0.16±0.51
p__Bacillota_B	0.16±0.23
p__Chlamydiota	0.11±0.53
p__Synergistota	0.03±0.16

6.1.2 Phylum relative abundances

phylum_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>%
  left_join(genome_metadata, by = join_by(genome == genome)) %>%
  group_by(sample,phylum) %>%
  summarise(relabun=sum(count))

phylum_summary %>%
    group_by(phylum) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T))  %>%
    mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(phylum,total) %>% 
    tt()

phylum	total
p__Bacillota_A	40.6±15.93
p__Bacteroidota	38.3±15.88
p__Pseudomonadota	5.83±7.87
p__Bacillota	4.59±6.37
p__Campylobacterota	3.47±5.63
p__Desulfobacterota	2.68±4.05
p__Verrucomicrobiota	1.53±2.43
p__Bacillota_C	0.95±1.05
p__Fusobacteriota	0.91±3.7
p__Cyanobacteriota	0.4±0.6
p__Spirochaetota	0.22±0.97
p__Bacillota_B	0.19±0.32
p__Actinomycetota	0.17±0.52
p__Chlamydiota	0.1±0.61
p__Deferribacterota	0.05±0.27
p__Synergistota	0.01±0.08

phylum_arrange <- phylum_summary %>%
    group_by(phylum) %>%
    summarise(mean=mean(relabun)) %>%
    arrange(-mean) %>%
    select(phylum) %>%
    pull()

phylum_summary %>%
    filter(phylum %in% phylum_arrange) %>%
    mutate(phylum=factor(phylum,levels=rev(phylum_arrange))) %>%
    ggplot(aes(x=relabun, y=phylum, group=phylum, color=phylum)) +
        scale_color_manual(values=phylum_colors[rev(phylum_arrange)]) +
        geom_jitter(alpha=0.5) + 
        theme_minimal()

6.2 Taxonomy boxplot

6.2.1 Family

family_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>% #append sample metadata
  left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,family) %>%
  summarise(relabun=sum(count))

family_summary %>%
    group_by(family) %>%
    summarise(mean=mean(relabun),sd=sd(relabun)) %>%
    arrange(-mean) %>%
    tt()

family	mean	sd
f__Lachnospiraceae	2.798160e-01	0.1323151296
f__Bacteroidaceae	2.132576e-01	0.1045524501
f__Tannerellaceae	1.122881e-01	0.0756060991
f__	6.556348e-02	0.0820773598
f__Helicobacteraceae	3.474580e-02	0.0562678198
f__Marinifilaceae	3.441228e-02	0.0255200299
f__UBA3700	2.777694e-02	0.0330290932
f__Desulfovibrionaceae	2.680077e-02	0.0404627923
f__Ruminococcaceae	2.435712e-02	0.0226880677
f__Rikenellaceae	1.900710e-02	0.0176572117
f__Erysipelotrichaceae	1.767814e-02	0.0269603016
f__Oscillospiraceae	1.696763e-02	0.0140960977
f__Coprobacillaceae	1.273032e-02	0.0336927310
f__Mycoplasmoidaceae	1.210259e-02	0.0270262454
f__Enterobacteriaceae	1.077111e-02	0.0651262303
f__Fusobacteriaceae	9.053249e-03	0.0370456782
f__Akkermansiaceae	8.677367e-03	0.0108836065
f__CAG-239	7.003283e-03	0.0098019806
f__LL51	6.656864e-03	0.0216352391
f__Anaerotignaceae	6.551732e-03	0.0074061779
f__UBA3830	5.185462e-03	0.0171469663
f__Gastranaerophilaceae	4.003188e-03	0.0059975426
f__Muribaculaceae	3.991492e-03	0.0409006227
f__Butyricicoccaceae	3.919222e-03	0.0050520985
f__CAG-274	3.051617e-03	0.0060828463
f__Acutalibacteraceae	2.723615e-03	0.0047471119
f__Anaerovoracaceae	2.683446e-03	0.0040557355
f__Pumilibacteraceae	2.544525e-03	0.0041701010
f__UBA1997	2.383785e-03	0.0080270055
f__CAG-508	2.324078e-03	0.0063109845
f__Brevinemataceae	2.191263e-03	0.0097068603
f__Peptococcaceae	1.937490e-03	0.0031961496
f__Rhodocyclaceae	1.936615e-03	0.0194735479
f__DTU072	1.792884e-03	0.0055918788
f__UBA660	1.742458e-03	0.0054664441
f__MGBC116941	1.714356e-03	0.0090980913
f__Massilibacillaceae	1.502892e-03	0.0024528334
f__Eggerthellaceae	1.377525e-03	0.0037174906
f__Enterococcaceae	8.454990e-04	0.0070848098
f__CALTSX01	5.198677e-04	0.0053270584
f__Chlamydiaceae	5.170637e-04	0.0030492819
f__Mucispirillaceae	4.593259e-04	0.0026630822
f__CALVMC01	4.496143e-04	0.0018583730
f__Clostridiaceae	4.212744e-04	0.0029132915
f__Acidaminococcaceae	4.004438e-04	0.0015777220
f__UBA1242	3.719350e-04	0.0014704441
f__RUG11792	3.717143e-04	0.0019425248
f__CAG-465	3.497207e-04	0.0015695529
f__Microbacteriaceae	3.199134e-04	0.0026999464
f__CAG-288	2.985206e-04	0.0017514912
f__Streptococcaceae	2.479473e-04	0.0018438849
f__Anaplasmataceae	2.435113e-04	0.0021508694
f__Hepatoplasmataceae	2.362221e-04	0.0024205565
f__Aeromonadaceae	2.327082e-04	0.0022486997
f__Peptostreptococcaceae	1.412306e-04	0.0014471826
f__Rhodobacteraceae	1.371859e-04	0.0014057373
f__Xanthomonadaceae	9.245878e-05	0.0009474206
f__Synergistaceae	7.840493e-05	0.0008034115
f__Lactobacillaceae	4.203166e-05	0.0004306963
f__Turicibacteraceae	0.000000e+00	0.0000000000

family_arrange <- family_summary %>%
    group_by(family) %>%
    summarise(mean=sum(relabun)) %>%
    arrange(-mean) %>%
    select(family) %>%
    pull()

family_summary %>%
    left_join(genome_metadata %>% select(family,phylum) %>% unique(),by=join_by(family==family)) %>%
    left_join(sample_metadata,by=join_by(sample==EHI_number)) %>%
    filter(family %in% family_arrange[1:20]) %>%
    mutate(family=factor(family,levels=rev(family_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=family, group=family, color=phylum)) +
        scale_color_manual(values=phylum_colors[-8]) +
        geom_jitter(alpha=0.5) + 
        theme_minimal() + 
        labs(y="Family", x="Relative abundance", color="Phylum")

6.2.2 Genus

genus_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata, by = join_by(sample == EHI_number)) %>% #append sample metadata
  left_join(genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,genus) %>%
  summarise(relabun=sum(count)) %>%
  filter(genus != "g__")

genus_summary %>%
    group_by(genus) %>%
    summarise(mean=mean(relabun),sd=sd(relabun)) %>%
    arrange(-mean) %>%
    tt()

genus	mean	sd
g__Bacteroides	1.725925e-01	0.0994470682
g__Parabacteroides	1.103381e-01	0.0767310570
g__Roseburia	5.077999e-02	0.0743624266
g__Phocaeicola	3.874581e-02	0.0435882803
g__JAAYNV01	3.526845e-02	0.0735025649
g__Odoribacter	3.376557e-02	0.0254845145
g__Helicobacter_J	2.983823e-02	0.0400574428
g__CAG-95	1.659217e-02	0.0240471187
g__Alistipes	1.555908e-02	0.0156740595
g__Kineothrix	1.544351e-02	0.0584815409
g__MGBC136627	1.451743e-02	0.0230914933
g__Mycoplasmoides	1.139564e-02	0.0269054026
g__Hungatella_A	1.113992e-02	0.0670243947
g__Anaerotruncus	1.006956e-02	0.0112599653
g__Velocimicrobium	9.687390e-03	0.0159382742
g__Enterocloster	9.506772e-03	0.0098451213
g__Fusobacterium_A	9.053249e-03	0.0370456782
g__Acetatifactor	8.989245e-03	0.0139417426
g__Akkermansia	8.677367e-03	0.0108836065
g__Clostridium_Q	7.770136e-03	0.0138182274
g__Bilophila	7.347777e-03	0.0088824113
g__Lawsonia	7.130440e-03	0.0356135290
g__Intestinimonas	6.565049e-03	0.0061282758
g__Lacrimispora	6.397950e-03	0.0078443852
g__Lachnotalea	5.679000e-03	0.0086820363
g__Desulfovibrio	5.604601e-03	0.0085133819
g__MGBC140009	5.533083e-03	0.0134005650
g__Extibacter	5.301402e-03	0.0438086011
g__Coprobacillus	5.255707e-03	0.0160178991
g__Ventrimonas	5.038137e-03	0.0083320432
g__NHYM01	4.907573e-03	0.0427691471
g__Dielma	4.863951e-03	0.0065480876
g__Eisenbergiella	4.698317e-03	0.0069699563
g__CHH4-2	4.463440e-03	0.0044701587
g__RGIG4733	4.252340e-03	0.0103379868
g__Negativibacillus	4.038946e-03	0.0054742770
g__Thomasclavelia	3.858196e-03	0.0117540652
g__Hungatella	3.440712e-03	0.0046402483
g__C-19	3.334625e-03	0.0097460323
g__Citrobacter	3.199667e-03	0.0207814167
g__UMGS1251	2.883830e-03	0.0066485409
g__Oscillibacter	2.697945e-03	0.0038453712
g__CAZU01	2.687821e-03	0.0062923086
g__Breznakia	2.569391e-03	0.0076690515
g__Copromonas	2.559264e-03	0.0037548588
g__Mailhella	2.486840e-03	0.0034349591
g__Pseudoflavonifractor	2.349829e-03	0.0028250595
g__Intestinibacillus	2.290036e-03	0.0027696754
g__Escherichia	2.232632e-03	0.0160096591
g__Brevinema	2.191263e-03	0.0097068603
g__MGBC165282	2.146060e-03	0.0051869977
g__Rikenella	2.123615e-03	0.0033608364
g__Morganella	2.019952e-03	0.0206983454
g__Robinsoniella	2.000942e-03	0.0198167695
g__Parabacteroides_B	1.950084e-03	0.0064473560
g__Hafnia	1.939718e-03	0.0112340944
g__Fluviibacter	1.936615e-03	0.0194735479
g__JAIHAL01	1.909337e-03	0.0043937983
g__CAJLXD01	1.809579e-03	0.0041385093
g__Marseille-P3106	1.729807e-03	0.0025326958
g__UBA866	1.535703e-03	0.0026149730
g__MGBC116941	1.433278e-03	0.0090996222
g__Duncaniella	1.426611e-03	0.0146184139
g__Stoquefichus	1.418761e-03	0.0046100976
g__Limenecus	1.393591e-03	0.0029887106
g__JAAYQI01	1.271605e-03	0.0020016472
g__RGIG6463	1.268390e-03	0.0028982477
g__Lawsonibacter	1.177407e-03	0.0016547788
g__Scatousia	1.174334e-03	0.0033329854
g__MGBC101980	1.147280e-03	0.0043856608
g__Hespellia	1.102861e-03	0.0080033094
g__Clostridium_AQ	1.075206e-03	0.0036972777
g__Tidjanibacter	1.062223e-03	0.0029392145
g__Fournierella	1.021882e-03	0.0022586979
g__Eggerthella	9.946416e-04	0.0031801193
g__OM05-12	9.566920e-04	0.0022952664
g__CALXRO01	9.555497e-04	0.0060396607
g__CALURL01	9.088777e-04	0.0021653921
g__Harryflintia	8.906013e-04	0.0020436518
g__MGBC133411	8.872215e-04	0.0021982622
g__Scatacola_A	8.685187e-04	0.0028129788
g__JALFVM01	8.392923e-04	0.0020111922
g__Bacteroides_G	8.358400e-04	0.0024999742
g__Ventrisoma	8.334289e-04	0.0015664780
g__CAG-269	8.185801e-04	0.0029513181
g__IOR16	8.132853e-04	0.0024147185
g__CAG-873	7.794020e-04	0.0079864937
g__Buttiauxella	7.562352e-04	0.0062186311
g__14-2	7.242943e-04	0.0014229865
g__Ureaplasma	7.069544e-04	0.0022811697
g__Scatocola	6.903112e-04	0.0024144468
g__Dysosmobacter	6.880800e-04	0.0012817741
g__Muricomes	6.843281e-04	0.0023723812
g__Anaerovorax	6.779999e-04	0.0017521412
g__UBA7185	6.690992e-04	0.0018509746
g__Butyricimonas	6.467068e-04	0.0016133034
g__MGBC131033	6.443020e-04	0.0015998136
g__Evtepia	6.309865e-04	0.0010453519
g__CAJMNU01	6.281598e-04	0.0008908563
g__Beduini	5.914594e-04	0.0013148807
g__Muribaculum	5.550053e-04	0.0056871120
g__Scandinavium	5.392506e-04	0.0034848792
g__Lactonifactor	5.340135e-04	0.0013169750
g__CALTSX01	5.198677e-04	0.0053270584
g__CAG-485	5.128011e-04	0.0052546481
g__Merdicola	5.109908e-04	0.0018183944
g__Ventrenecus	4.954074e-04	0.0024443063
g__UMGS1202	4.885966e-04	0.0016971062
g__Copranaerobaculum	4.827706e-04	0.0029167211
g__NSJ-61	4.574747e-04	0.0014044413
g__Faecimonas	4.467204e-04	0.0017621834
g__RGIG8482	4.415175e-04	0.0020991286
g__Faecivivens	4.313538e-04	0.0008927244
g__RGIG9287	4.228193e-04	0.0021002237
g__Sarcina	4.212744e-04	0.0029132915
g__Blautia_A	4.144742e-04	0.0010034450
g__Scatenecus	4.103021e-04	0.0026534948
g__Phascolarctobacterium	4.004438e-04	0.0015777220
g__Raoultibacter	3.828831e-04	0.0011609168
g__Caccovivens	3.719350e-04	0.0014704441
g__CAJTFG01	3.690163e-04	0.0010223015
g__HGM11386	3.642876e-04	0.0016060882
g__CAG-465	3.497207e-04	0.0015695529
g__Amedibacillus	3.457091e-04	0.0023834921
g__Enterococcus_A	3.276668e-04	0.0023252026
g__UMGS2016	3.212305e-04	0.0012912770
g__Emergencia	3.205926e-04	0.0009702487
g__Holdemania	3.098282e-04	0.0010287045
g__Blautia	3.095400e-04	0.0011077317
g__Protoclostridium	3.067429e-04	0.0010837140
g__Fimivivens	3.043536e-04	0.0008104254
g__RGIG7389	2.985465e-04	0.0005848732
g__CAG-345	2.985206e-04	0.0017514912
g__UBA7173	2.963722e-04	0.0030369115
g__Bariatricus	2.928468e-04	0.0008379528
g__Agathobaculum	2.787287e-04	0.0016389968
g__CALXDZ01	2.660973e-04	0.0006132220
g__UBA940	2.621830e-04	0.0009075589
g__Microbacterium	2.563077e-04	0.0026263723
g__Aminipila	2.506050e-04	0.0007479203
g__Lactococcus	2.479473e-04	0.0018438849
g__Wolbachia	2.435113e-04	0.0021508694
g__Paramuribaculum	2.432436e-04	0.0024925056
g__Hepatoplasma	2.362221e-04	0.0024205565
g__Aeromonas	2.327082e-04	0.0022486997
g__WRHT01	2.186196e-04	0.0006955370
g__Zhenpiania	2.116559e-04	0.0012708329
g__UBA5026	2.112884e-04	0.0009778887
g__UMGS1663	1.999591e-04	0.0007286034
g__MGBC107952	1.752651e-04	0.0009887232
g__CALXEL01	1.725652e-04	0.0013728689
g__CAG-273	1.482564e-04	0.0007864691
g__Clostridioides	1.412306e-04	0.0014471826
g__Paracoccus	1.371859e-04	0.0014057373
g__JAAWBF01	1.286984e-04	0.0006144643
g__JAFLTL01	1.270703e-04	0.0013020827
g__Bacteroides_H	1.267213e-04	0.0012985068
g__RUG12867	9.254620e-05	0.0006129332
g__Stenotrophomonas	9.245878e-05	0.0009474206
g__Rahnella	8.365618e-05	0.0008059384
g__Lumbricidophila	6.360575e-05	0.0006517650
g__UBA3263	5.098641e-05	0.0005224553
g__Fructobacillus	4.203166e-05	0.0004306963
g__Clostridium	0.000000e+00	0.0000000000
g__Turicibacter	0.000000e+00	0.0000000000

genus_arrange <- genus_summary %>%
    group_by(genus) %>%
    summarise(mean=sum(relabun)) %>%
    filter(genus != "g__")%>%
    arrange(-mean) %>%
    select(genus) %>%
    mutate(genus= sub("^g__", "", genus)) %>%
    pull()

genus_summary %>%
    left_join(genome_metadata %>% select(genus,phylum) %>% unique(),by=join_by(genus==genus)) %>%
    left_join(sample_metadata,by=join_by(sample==EHI_number)) %>%
    mutate(genus= sub("^g__", "", genus)) %>%
    filter(genus %in% genus_arrange[1:20]) %>%
    mutate(genus=factor(genus,levels=rev(genus_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=genus, group=genus, color=phylum)) +
        scale_color_manual(values=phylum_colors) +
        geom_jitter(alpha=0.5) + 
        theme_minimal() + 
        labs(y="Genus", x="Relative abundance", color="Phylum")